Integrating the GPCR transactivation‐dependent and biased signalling paradigms in the context of PAR1 signalling
Bibliographic record
Abstract
Classically, receptor-mediated signalling was conceived as a linear process involving one agonist, a variety of potential targets within a receptor family (e.g. α- and β-adrenoceptors) and a second messenger (e.g. cAMP)-triggered response. If distinct responses were stimulated by the same receptor in different tissues (e.g. lipolysis in adipocytes vs. increased beating rate in the heart caused by adrenaline), the differences were attributed to different second messenger targets in the different tissues. It is now realized that an individual receptor can couple to multiple effectors (different G proteins and different β-arrestins), even in the same cell, to drive very distinct responses. Furthermore, tailored agonists can mould the receptor conformation to activate one signal pathway versus another by a process termed 'biased signalling'. Complicating issues further, we now know that activating one receptor can rapidly trigger the local release of agonists for a second receptor via a process termed 'transactivation'. Thus, the end response can represent a cooperative signalling process involving two or more receptors linked by transactivation. This overview, with a focus on the GPCR, protease-activated receptor-1, integrates both of these processes to predict the complex array of responses that can arise when biased receptor signalling also involves the receptor transactivation process. The therapeutic implications of this signalling matrix are also briefly discussed. Linked Articles This article is part of a themed section on Molecular Pharmacology of G Protein-Coupled Receptors. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v173.20/issuetoc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".